Author(s): Zongcheng Li; Yasi Zhang
Reviewer(s): Ying Ge
Date: 2025-05-20
画出这种连线图。
Draw this connection diagram.
出自:https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-021-01322-w,跟FigureYa260CNV出自同一篇文章
图5 与WM评分相关的转录及转录后调控特征。 a TCGA-COAD/READ队列中WM评分高/低组间miRNA靶向信号通路的差异。红线表示高WM评分组中低表达的miRNA,蓝线表示低WM评分组中高表达的miRNA。红点对应高WM评分组中高表达的miRNA靶基因,蓝点对应低WM评分组中高表达的miRNA靶基因。圆圈代表靶基因富集的信号通路。
Source: https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-021-01322-w, from the same article as FigureYa260CNV.
Fig. 5 Transcriptional and post-transcriptional characteristics associated with the WM_Score. a Differences in miRNA-targeted signaling pathways in the TCGA-COAD/READ cohort between the WM_Score-high and -low groups. The red line represents a low expression of miRNA in the high WM_Score group, and the blue line represents a high expression of miRNA in the low WM_Score group. Red dots correspond to miRNA-targeted genes highly expressed in the high WM_Score group, and blue dots correspond to miRNA-targeted genes highly expressed in the low WM_Score group. The circle represents a signaling pathway enriched with targeted genes.
类似的图:
A similar image:
出自:https://doi.org/10.1038/s42255-019-0045-8,跟FigureYa174squareCross、FigureYa199crosslink、FigureYa256panelLink出自同一篇文章。这篇文章以连线著称,总是被模仿,不知道会不会被超越。
图3 | 倾向性评分算法概述及跨癌种低氧相关分子模式。 c图展示1,074个癌细胞系中低氧相关基因mRNA表达水平与药物敏感性之间的斯皮尔曼等级相关性。x轴上的深绿色圆点代表低氧相关基因;橙色圆点表示按不同信号通路聚类的药物。橙色圆点的大小反映与药物敏感性相关基因的数量(|rs| > 0.3且FDR < 0.05);条形图显示与基因存在相关性的药物数量。粉色与青色线条分别表示正相关与负相关。JNK指Jun N末端激酶。
Source: https://doi.org/10.1038/s42255-019-0045-8, from the same article as FigureYa174squareCross, FigureYa199crosslink, and FigureYa256panelLink. This paper is renowned for its connection designs—constantly imitated, yet to be surpassed.
Fig. 3 | overview of the propensity score algorithm and the hypoxia-associated molecular patterns across cancer types. c, Association between mRNA expression levels of hypoxia-associated genes and drug sensitivity across 1,074 cancer cell lines by Spearman’s rank correlation. The dark green dots along the x axis indicate hypoxia-related genes; the orange dots denote drugs that are clustered by different signalling pathways. The size of the orange dot indicates the number of genes correlated with drug sensitivity (|rs| > 0.3, FDR < 0.05); the bar plot shows the number of drugs correlated with the genes. The pink and cyan lines indicate positive and negative correlation, respectively. JNK, Jun N-terminal kinase.
展示miRNA-靶基因(或基因-药物等)的关系,连线和节点的颜色代表节点类型(例如例文的high和low WM_Score)。同一通路的基因画在同一圆圈里,并标注通路名。
为了画这个图,完善了crosslink包,该R包会继续添加更多有趣的连线功能,感兴趣可前往https://github.com/zzwch/crosslink查看最新版本及功能,在github上还能提交issue跟作者直接交流。
This figure displays miRNA-target gene (or gene-drug, etc.) relationships, where the colors of connecting lines and nodes represent node types (e.g., high vs. low WM_Score as shown in the example). Genes from the same pathway are grouped within circular clusters labeled with pathway names.
To create this visualization, we enhanced the crosslink R package, which will continue to incorporate more innovative connection features. Those interested can visit https://github.com/zzwch/crosslink to explore the latest version and functionalities. GitHub also allows users to submit issues for direct communication with the author.
使用国内镜像安装包。
Using domestic mirrors for package installation.
options("repos"= c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="http://mirrors.tuna.tsinghua.edu.cn/bioconductor/")
# 安装crosslink(确保按照最新版本)
# Install crosslink (ensure you're using the latest version)
# remotes::install_github("zzwch/crosslink", build_vignettes = TRUE)
library(magrittr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks magrittr::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::set_names() masks magrittr::set_names()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(crosslink)
##
## Attaching package: 'crosslink'
##
## The following object is masked from 'package:purrr':
##
## list_along
# 显示英文报错信息
# Show English error messages
Sys.setenv(LANGUAGE = "en")
# 禁止chr转成factor
# Prevent character-to-factor conversion
options(stringsAsFactors = FALSE)
easy_input_links.csv,连线表示source(miRNA)和target(靶基因)的关系。连线的颜色表示source的类型source_type(high WM_Score和low WM_Score)。
easy_input_nodes.csv,key(包括source和target)所在的path(通路)信息。
# size = size前面的#删掉。easy_input_links.csv represents the relationship between the source (miRNA) and the target (target gene), where the color of the line indicates the type of the source (source_type: high WM_Score and low WM_Score).
easy_input_nodes.csv contains the pathway information (path) for the keys (including both source and target).
# in
# size = size in the document.links <- read.csv("easy_input_links.csv")
nodes <- read.csv("easy_input_nodes.csv")
# 获取所有通路的名字
# Get all pathway names
paths <- unique(nodes[nodes$path != "source", ]$path)
# 把"source"排在前面
# Place "source" first
nodes$path <- factor(nodes$path, levels = c("source", paths))
# 连线的颜色
# Line colors
src_up_col <- "red"
src_dn_col <- "blue"
# target节点的颜色
# Target node colors
tar_up_col <- "red"
tar_dn_col <- "blue"
重要提示!节点和边数据中不能包含列名 ‘node’、‘cross’、‘node.type’、‘x’、‘y’、‘degree’!
IMPORTANT! The colnames of ‘node’, ‘cross’, ‘node.type’, ‘x’, ‘y’, ‘degree’ MUST NOT BE included in nodes and edges!
# 使用crosslink函数创建网络布局对象
# Create network layout object using crosslink function
toy <- crosslink(
nodes = nodes,
edges = links,
cross.by = "path",
xrange = c(0, 10),
yrange = c(-5, 5),
spaces = "partition")
# 绘制网络布局图
# Plot the network layout
cl_plot(toy)
# 自定义函数
# Custom function
toCircle <- function(x, y, rx = 1, ry =1, intensity = 2){
mapTo2pi <- function(x) {scales::rescale(c(0, x), to = c(0, 2*pi))[-1]}
data.frame(x, y) %>%
mutate(group = paste0("group", x)) %>%
mutate(yy = scales::rescale(-x, to = range(y))) %>%
mutate(xx = mean(x) + intensity * sin(yy %>% mapTo2pi),) %>%
group_by(group) %>%
mutate(tri = rank(y, ties.method = "first") %>% mapTo2pi) %>%
ungroup() %$%
data.frame(
x = xx + rx*sin(tri),
y = yy + ry*cos(tri))
}
# 应用圆形变换函数到网络布局对象
# Apply circular transformation to network layout object
toy_circle <- toy %>% tf_fun(
crosses = paths,
along = "xy",
fun = toCircle,
rx = 0.2, ry = 0.2)
# 绘制变换后的网络图(不显示标签)
# Plot transformed network (without labels)
toy_circle %>% cl_plot(label = NA)
## 对圆形布局进行几何变换
## Geometric transformations for circular layout
toy_final <- toy_circle %>%
tf_rotate(angle = -90) %>%
tf_flip(axis = "x", crosses = paths) %>%
tf_shift(y = 8, crosses = paths, relative = F) %>%
set_header()
## 可视化后处理结果
## Visualize post-processing results
toy_final %>% cl_plot(label = NA) %>% cl_void()
# 显示可用图形属性
# Display available graphic attributes
show_aes(toy_final)
## Available meta.data names are showing below.
## Cross: node, node.type, x, y, cross, key, type, path, signif, degree
## Link: src, tar, source_type, src.cross, tar.cross, source, target, src.degree, tar.degree, x, y, xend, yend
## Header: node, node.type, x, y, cross, header
ggplot() +
# 每个模块相对独立,可根据需要调整不同图层的叠加顺序
# Each module is relatively independent, you can adjust the layer stacking order as needed
# 路径的黑色圆圈(绘制在最底层,部分会被靶点覆盖)
# Black circles for pathways (drawn at the bottom layer, partially covered by target points)
ggforce::geom_circle(
mapping = aes(x0 = x0, y0 = y0, r = r),
data = get_cross(toy_final) %>% filter(cross != "source") %>%
group_by(path) %>%
transmute(
x0 = mean(x),
y0 = mean(y),
r = 0.2
) %>% unique(),
show.legend = F
) +
# 连线(miRNA-靶基因关系)
# Connection lines (miRNA-target relationships)
geom_segment(
mapping = aes(x, y, xend = xend, yend = yend, color = source_type),
data = get_link(toy_final),
alpha = 0.3
) +
# 靶点节点
# Target nodes
geom_point(
mapping = aes(x, y,
# size = size,
color = type),
data = get_cross(toy_final) %>% filter(cross != "source")
) +
# 添加文字:靶点所属通路名称
# Add text: Pathway names for targets
ggrepel::geom_text_repel(
mapping = aes(x, y, label = header), nudge_y = 0.3,
data = get_header(toy_final) %>% filter(cross != "source"),
segment.color = NA
) +
# 添加文字:miRNA源节点名称
# Add text: miRNA source node names
geom_text(
mapping = aes(x, y, label = key), angle = 90, hjust = 1, nudge_y = -0.1,
data = get_cross(toy_final) %>% filter(cross == "source")
) +
# 添加文字:每个通路的靶点数量
# Add text: Number of targets per pathway
geom_text(
mapping = aes(x, y, label = num),
data = get_cross(toy_final) %>% filter(cross != "source") %>%
group_by(path) %>%
transmute(
x = mean(x),
y = mean(y),
num = n()
) %>% unique()
) +
# 颜色配置
# Color settings
scale_color_manual(values = c(
src_up = src_up_col, src_dn = src_dn_col,
tar_up = tar_up_col, tar_dn = tar_dn_col)) +
labs(x = NULL, y = "Target_Pathway") +
scale_y_continuous(expand = expansion(mult = c(0.25,0.1))) -> p
p
如果想要像例文2那样给source也画上点,就运行下面这段
If you want to plot points for the source nodes as in Example 2, run the following code
# 画source节点
# Plot source nodes
p <- p + geom_point(
mapping = aes(x, y),
data = get_cross(toy_final) %>% filter(cross == "source")
)
把source的’signif’标注在source名字的下方
Place the ‘signif’ annotation below the source names
# 创建带注释的circLink图
# Create circLink plot with annotations
cl_plot2(
p %>% cl_void(th = theme(
axis.title = element_text())),
object = toy_final,
annotation = cl_annotation(
bottom = ggplot() +
geom_text(
mapping = aes(seq_along(key), 0, label = signif),
data = nodes %>% filter(path == "source")
) + theme_void()
,
bottom.by = "source", bottom.height = 0.05
)
)
# 保存图形为PDF文件
# Save plot as PDF file
ggsave("circLink.pdf", width = 10, height = 5)
# 生成节点名称
# Generate node names
sources <- paste0("source", 1:20 %>% format)
targets <- paste0("target", 1:500 %>% format)
paths <- paste0("path", 1:15 %>% format)
# 创建节点数据框
# Create node dataframe
nodes <- data.frame(
key = c(sources, targets),
type = c(rep("src_up", length(sources)/2),
rep("src_dn", length(sources)/2),
sample(c("tar_up", "tar_dn"), length(targets), replace = T)),
path = c(rep("source", length(sources)),
rep(paths, times = c(
40, 50, 30, 30, 50, 50, 20, 30, 30, 40, 20, 30, 30, 20, 30
))) %>% factor(
levels = c("source", paths)
),
signif = c(sample(c("*", "**", "***", "ns"), length(sources), replace = T),
rep(NA, length(targets)))
)
# 生成连接关系数据
# Generate link relationships
link_n <- 500
set.seed(666)
links <- data.frame(
src = sample(sources, link_n, replace = T),
tar = sample(targets, link_n, replace = T)) %>%
unique() %>%
mutate(source_type = nodes$type[match(src, nodes$key)])
# 保存示例数据文件
# Save example data files
write.csv(links, "easy_input_links.csv", row.names = F, quote = F)
write.csv(nodes, "easy_input_nodes.csv", row.names = F, quote = F)
# 显示会话信息
# Show session information
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Asia/Shanghai
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crosslink_0.1.0 lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1
## [5] dplyr_1.1.4 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
## [9] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] yulab.utils_0.2.0 sass_0.4.10 generics_0.1.4 ggplotify_0.1.2
## [5] stringi_1.8.7 hms_1.1.3 digest_0.6.37 evaluate_1.0.3
## [9] grid_4.4.1 timechange_0.3.0 RColorBrewer_1.1-3 fastmap_1.2.0
## [13] jsonlite_2.0.0 ggrepel_0.9.6 aplot_0.2.5 scales_1.4.0
## [17] tweenr_2.0.3 textshaping_1.0.1 jquerylib_0.1.4 cli_3.6.5
## [21] rlang_1.1.6 polyclip_1.10-7 withr_3.0.2 cachem_1.1.0
## [25] yaml_2.3.10 tools_4.4.1 tzdb_0.5.0 gridGraphics_0.5-1
## [29] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4 ggfun_0.1.8
## [33] fs_1.6.6 MASS_7.3-61 ragg_1.4.0 pkgconfig_2.0.3
## [37] pillar_1.10.2 bslib_0.9.0 gtable_0.3.6 glue_1.8.0
## [41] Rcpp_1.0.14 systemfonts_1.2.3 ggforce_0.4.2 xfun_0.52
## [45] tidyselect_1.2.1 rstudioapi_0.17.1 knitr_1.50 dichromat_2.0-0.1
## [49] farver_2.1.2 htmltools_0.5.8.1 patchwork_1.3.0 rmarkdown_2.29
## [53] labeling_0.4.3 compiler_4.4.1